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"name": "stdout",
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"text": [
"/kaggle/input/question-pairs-dataset/questions.csv\n"
]
}
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"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
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],
"source": [
"import torch\n",
"\n",
"torch.cuda.is_available()"
]
},
{
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{
"ename": "AssertionError",
"evalue": "Torch not compiled with CUDA enabled",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_19/361484350.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36mcurrent_device\u001b[0;34m()\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcurrent_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34mr\"\"\"Returns the index of a currently selected device.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 481\u001b[0;31m \u001b[0m_lazy_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 482\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_getDevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m 208\u001b[0m \"multiprocessing, you must use the 'spawn' start method\")\n\u001b[1;32m 209\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_cuda_getDeviceCount'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Torch not compiled with CUDA enabled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 211\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_cudart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m raise AssertionError(\n",
"\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
]
}
],
"source": [
"torch.cuda.current_device()"
]
},
{
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{
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"metadata": {
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"source": [
"!pip install sentence_transformers"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "795a7291",
"metadata": {
"execution": {
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"source": [
"from sentence_transformers import SentenceTransformer\n",
"\n",
"model = SentenceTransformer('all-MiniLM-L6-v2')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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},
"outputs": [],
"source": [
"df_q = pd.read_csv(\"/kaggle/input/question-pairs-dataset/questions.csv\")\n",
"df_q.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "948cc64f",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"df_q.isnull().any(axis=1).sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db457f33",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [],
"source": [
"print(df_q.shape)\n",
"df_q.dropna(subset=['question1', 'question2'], inplace=True)\n",
"print(df_q.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3bccc42f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-11-09T19:31:24.541791Z",
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"tags": []
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"outputs": [],
"source": [
"def create_embeddings(inputs):\n",
" print(\"encoding with model\")\n",
" sentence_embeddings = model.encode(inputs, show_progress_bar=True)\n",
" return sentence_embeddings\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c4d7d4ef",
"metadata": {
"execution": {
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},
"tags": []
},
"outputs": [],
"source": [
"from scipy import spatial\n",
"\n",
"def calc_similarity(embeddings1, embeddings2):\n",
" cosine_distance = spatial.distance.cosine(embeddings1, embeddings2)\n",
" cosine_similarity = 1 - cosine_distance\n",
" return cosine_similarity\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aff594c1",
"metadata": {
"execution": {
"iopub.execute_input": "2022-11-09T19:31:37.972353Z",
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},
"tags": []
},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"tqdm.pandas()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c71b2d18",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [],
"source": [
"questions_1 = list(df_q[\"question1\"].values)\n",
"embeddings_1 = create_embeddings(questions_1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52dcc871",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [],
"source": [
"questions_2 = list(df_q[\"question2\"].values)\n",
"embeddings_2 = create_embeddings(questions_2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8aa9bcec",
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"tags": []
},
"outputs": [],
"source": [
"32*12636"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3d9e28e",
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"tags": []
},
"outputs": [],
"source": [
"similarities = []\n",
"for row in tqdm(zip(embeddings_1, embeddings_2), total=len(embeddings_1)):\n",
" similarities.append(calc_similarity(row[0], row[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34617149",
"metadata": {
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"outputs": [],
"source": [
"df_compare = df_q.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2146800f",
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"tags": []
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"outputs": [],
"source": [
"df_compare[\"similarity\"] = similarities"
]
},
{
"cell_type": "code",
"execution_count": null,
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"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"df_compare[\"abs_similarity\"] = np.abs(similarities)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "343fd58a",
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},
"outputs": [],
"source": [
"df_compare.head()"
]
},
{
"cell_type": "code",
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"tags": []
},
"outputs": [],
"source": [
"min(df_compare[\"similarity\"])"
]
},
{
"cell_type": "code",
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"tags": []
},
"outputs": [],
"source": [
"max(df_compare[\"similarity\"])"
]
},
{
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},
"outputs": [],
"source": [
"min(df_compare[\"abs_similarity\"])"
]
},
{
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"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"def print_n_top(n, df):\n",
" indices = []\n",
" for x in range(n):\n",
" q1 = df.iloc[x][\"question1\"]\n",
" q2 = df.iloc[x][\"question2\"]\n",
" sim = df.iloc[x][\"similarity\"]\n",
" abs_sim = df.iloc[x][\"abs_similarity\"]\n",
" idx = df.index[x]\n",
" indices.append(idx)\n",
" print(f\"{q1} vs\\n{q2}\\n - {sim}:{abs_sim} index={idx}\\n\")\n",
" print(indices)\n",
" return indices"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3b5b993",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"#thresold=1 -> no filtering\n",
"def filter_and_print_top(df, threshold=1, threshold_up=False, col=\"similarity\", ascending=False, n=5):\n",
" if threshold_up:\n",
" df_filtered = df[df[col] > threshold]\n",
" else:\n",
" df_filtered = df[df[col] < threshold]\n",
"\n",
" dropped_size = df.shape[0] - df_filtered.shape[0]\n",
" print(f\"dropped after filtering: {dropped_size}\")\n",
" df_filtered = df_filtered.sort_values(by=col, ascending=ascending)\n",
" indices = print_n_top(n, df_filtered)\n",
" return indices"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "720a2598",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"n = 20\n",
"most_similar_indices = filter_and_print_top(df_compare, ascending=False, n=n)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd8e167d",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"most_dissimilar_indices = filter_and_print_top(df_compare, ascending=True, n=n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25ad79ce",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"filter_and_print_top(df_compare, col=\"abs_similarity\", ascending=True, n=n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4d8b2dc",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"filter_and_print_top(df_compare, threshold=0.99, n=n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b3d0421",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"most_similar_indices_90 = filter_and_print_top(df_compare, threshold=0.90, n=n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87040227",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_compare[\"prediction\"] = df_compare[\"similarity\"] > 0.9\n",
"df_compare[\"prediction\"] = df_compare[\"prediction\"].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b39d245",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_compare.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73f1cb3e",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sum(df_compare[\"is_duplicate\"] == df_compare[\"prediction\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bbc8d34",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_compare.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cb44e7e",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"301950/404348"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59e74061",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"def calculate_accuracy(df, threshold):\n",
" df[\"prediction\"] = df[\"similarity\"] > threshold\n",
" df[\"prediction\"] = df[\"prediction\"].astype(int)\n",
" correct = sum(df_compare[\"is_duplicate\"] == df_compare[\"prediction\"])\n",
" total = df.shape[0]\n",
" accuracy = correct / total\n",
" return correct, accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92a31ddc",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"thresholds = []\n",
"accuracies = []\n",
"corrects = []\n",
"\n",
"for threshold in range(1, 100, 1):\n",
" threshold = threshold / 100.0\n",
" correct, accuracy = calculate_accuracy(df_compare, threshold)\n",
" thresholds.append(threshold)\n",
" accuracies.append(accuracy)\n",
" corrects.append(correct)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18f7a495",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_accuracy = pd.DataFrame()\n",
"df_accuracy[\"threshold\"] = thresholds\n",
"df_accuracy[\"accuracy\"] = accuracies\n",
"df_accuracy[\"correct\"] = corrects"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d65eefd",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_accuracy[\"accuracy\"].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43b201f0",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"np.max(df_accuracy[\"accuracy\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "778ba817",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"np.argmax(df_accuracy[\"accuracy\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78f85dc4",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"pca = PCA(n_components=2)\n",
"pca_components = pca.fit_transform(embeddings_1)\n",
"print(pca.explained_variance_ratio_)\n",
"df_pca = pd.DataFrame(data = pca_components, columns = ['pca1', 'pca2'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56ae7101",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"plt.scatter(df_pca[\"pca1\"],df_pca[\"pca2\"])\n",
"#plt.scatter(data2[0],data2[1])\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "365cd887",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"pca = PCA(n_components=3)\n",
"pca_components = pca.fit_transform(embeddings_1)\n",
"print(pca.explained_variance_ratio_)\n",
"df_pca = pd.DataFrame(data = pca_components, columns = ['pca1', 'pca2', 'pca3'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f79fedb",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"pca = PCA(n_components=50)\n",
"pca_components = pca.fit_transform(embeddings_1)\n",
"print(pca.explained_variance_ratio_)\n",
"#df_pca = pd.DataFrame(data = pca_components, columns = ['pca1', 'pca2', 'pca3'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b45ac2a",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"import numpy as np\n",
"from sklearn.manifold import TSNE\n",
"#from cuml.manifold import TSNE\n",
"\n",
"tsne = TSNE(n_components=2, learning_rate='auto', init='random', perplexity=3, verbose=1)\n",
"tsne_embedded = tsne.fit_transform(pca_components)\n",
"tsne_embedded.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83895d41",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"from umap import UMAP\n",
"import plotly.express as px\n",
"\n",
"print(\"creating 2d UMAP component\")\n",
"umap_2d = UMAP(n_components=2, init='random', random_state=0)\n",
"print(\"creating 3d UMAP component\")\n",
"umap_3d = UMAP(n_components=3, init='random', random_state=0)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "014ee28f",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"print(\"fitting 2d UMAP\")\n",
"proj_2d = umap_2d.fit_transform(embeddings_1)\n",
"\n",
"print(\"fitting 3d UMAP\")\n",
"proj_3d = umap_3d.fit_transform(embeddings_1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ce3e233",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"print(\"creating 2d scatter\")\n",
"fig_2d = px.scatter(\n",
" proj_2d, x=0, y=1,\n",
" #color=[per dot color in array], labels={'color': 'species'}\n",
")\n",
"print(\"creating 3d scatter\")\n",
"fig_3d = px.scatter_3d(\n",
" proj_3d, x=0, y=1, z=2,\n",
" #color=[per dot color in array], labels={'color': 'species'}\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a7ff5b8",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"print(\"updating traces\")\n",
"fig_3d.update_traces(marker_size=5)\n",
"\n",
"print(\"showing figures\")\n",
"#fig_2d.show()\n",
"#fig_3d.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fb61ef3",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_sorted = df_compare.sort_values(by=\"similarity\")\n",
"df_sorted.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a67cfdd",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_sorted.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1eb01c4f",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"#filter_indices_top=[241636, 3099, 359487, 102181, 186992]\n",
"filter_indices_top = most_similar_indices\n",
"top_embeddings = embeddings_1[filter_indices_top]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "427579ed",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"#filter_indices_bottom=[74220, 54367, 314641, 218913, 282911]\n",
"filter_indices_bottom=most_dissimilar_indices\n",
"bottom_embeddings = embeddings_1[filter_indices_bottom]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0346bb3e",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"filter_indices = filter_indices_top + filter_indices_bottom"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20df5b6a",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"top_bottom_embeddings = np.concatenate([top_embeddings, bottom_embeddings])\n",
"top_bottom_embeddings.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "197e9ad9",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"colors = [\"1\"]*len(top_embeddings) + [\"2\"]*len(bottom_embeddings)\n",
"colors"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3ce241b",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"print(\"fitting 2d UMAP\")\n",
"#proj_2d = umap_2d.fit_transform(top_bottom_embeddings)\n",
"proj_2d = umap_2d.fit_transform(embeddings_1)\n",
"\n",
"print(\"fitting 3d UMAP\")\n",
"#proj_3d = umap_3d.fit_transform(top_bottom_embeddings)\n",
"proj_3d = umap_3d.fit_transform(embeddings_1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d4425bc",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"proj_2d_slice = proj_2d[filter_indices]\n",
"proj_3d_slice = proj_3d[filter_indices]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85e94ddc",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"print(\"creating 2d scatter\")\n",
"fig_2d = px.scatter(\n",
" proj_2d_slice, x=0, y=1,\n",
" color=colors, labels={'color': 'species'}\n",
")\n",
"print(\"creating 3d scatter\")\n",
"fig_3d = px.scatter_3d(\n",
" proj_3d_slice, x=0, y=1, z=2,\n",
" color=colors, labels={'color': 'species'}\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8ebe19d",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"print(\"updating traces\")\n",
"fig_3d.update_traces(marker_size=5)\n",
"\n",
"print(\"showing figures\")\n",
"fig_2d.show()\n",
"fig_3d.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f7faf0c",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"pca = PCA(n_components=2)\n",
"pca_components = pca.fit_transform(embeddings_1)\n",
"print(pca.explained_variance_ratio_)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3149e57",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_pca_top = pd.DataFrame(data = pca_components[filter_indices_top], columns = ['pca1', 'pca2'])\n",
"df_pca_top[\"type\"] = 1\n",
"df_pca_bottom = pd.DataFrame(data = pca_components[filter_indices_bottom], columns = ['pca1', 'pca2'])\n",
"df_pca_bottom[\"type\"] = 2\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79b87613",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"plt.scatter(df_pca_top[\"pca1\"],df_pca_top[\"pca2\"])\n",
"plt.scatter(df_pca_bottom[\"pca1\"],df_pca_bottom[\"pca2\"])\n",
"#plt.scatter(data2[0],data2[1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d23b9e6f",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"pca = PCA(n_components=3)\n",
"#pca = pca.fit(embeddings_1)\n",
"pca_components = pca.fit_transform(embeddings_1)\n",
"print(pca.explained_variance_ratio_)\n",
"df_pca = pd.DataFrame(data = pca_components, columns = ['pca1', 'pca2', 'pca3'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f81dac1",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_pca_top = pd.DataFrame(data = df_pca.iloc[filter_indices_top], columns = ['pca1', 'pca2', 'pca3'])\n",
"df_pca_bottom = pd.DataFrame(data = df_pca.iloc[filter_indices_bottom], columns = ['pca1', 'pca2', 'pca3'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8bd9ed3e",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"plt.scatter(df_pca_top[\"pca1\"],df_pca_top[\"pca2\"])\n",
"plt.scatter(df_pca_bottom[\"pca1\"],df_pca_bottom[\"pca2\"])\n",
"#plt.scatter(data2[0],data2[1])\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66715ff3",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_all = pd.concat([df_pca_top, df_pca_bottom], axis=0)\n",
"df_all.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21b28549",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"import plotly.express as px\n",
"\n",
"#fig = px.scatter_3d(df_pca, x='pca1', y='pca2', z='pca3', color=colors, labels={'color': 'species'})\n",
"fig = px.scatter_3d(df_all, x='pca1', y='pca2', z='pca3', color=colors)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "535651b8",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"#taking slot 0 is pointless as the question is exact same in both embeddings (the first question)\n",
"#thus index 1 = question 2\n",
"cosine_scores = util.cos_sim(embeddings_1[1], embeddings_2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e58581a7",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"arr = np.array(cosine_scores)\n",
"arr[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a158a832",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"best_match_idx = np.argmax(arr[0])\n",
"best_match_idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d9f85e8",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"#need to +1 the index since 330777 starting from 1: is actual 330778 as 0 was skilled by 1:\n",
"arr[0][best_match_idx]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2e9bbf0",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_q.iloc[best_match_idx]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3dbb6776",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"calc_similarity(embeddings_1[0], embeddings_2[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d377f730",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"util.cos_sim(embeddings_1[0], embeddings_2[0])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65b0e681",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"reference = embeddings_1[1]\n",
"bext_idx = 0\n",
"highest_sim = -100\n",
"for idx, embed_2 in tqdm(enumerate(embeddings_2)):\n",
" sim = calc_similarity(reference, embed_2)\n",
" if sim > highest_sim:\n",
" highest_sim = sim\n",
" best_idx = idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "31cdefbe",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"best_idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d34d2d18",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_q.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f985742f",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 11.129218,
"end_time": "2022-11-11T13:49:53.270814",
"environment_variables": {},
"exception": true,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2022-11-11T13:49:42.141596",
"version": "2.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0110/704/110704507.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bb1c8756\",\n \"metadata\": (...TRUNCATED) | 0110/704/110704873.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0110/704/110704935.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"name\":\"python3\",\"display_name\":\"Python 3\",\"language\":\"py(...TRUNCATED) | 0110/704/110704987.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eab3825e\",\n \"metadata\": (...TRUNCATED) | 0110/705/110705276.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"460708(...TRUNCATED) | 0110/705/110705353.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0110/705/110705574.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0110/705/110705674.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9fc0addc\",\n \"metadata\": (...TRUNCATED) | 0110/705/110705792.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"d8a0bc(...TRUNCATED) | 0110/705/110705803.ipynb | s3://data-agents/kaggle-outputs/sharded/026_00110.jsonl.gz |
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